{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e56293d-5e9f-444e-b638-21f17ca3f63c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Habitable Zone\n",
    "\n",
    "宜居带是指恒星附近可以存在类地行星并且有液态水的区域。\n",
    "\n",
    "我们尝试用 273-373K 的黑体温度来对太阳周围的宜居带进行半径估计。"
   ]
  },
  {
   "cell_type": "code",
   "id": "ba903ab0-bcb3-4fe2-958f-6fe15eef2876",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.416596Z",
     "start_time": "2024-11-20T06:58:11.091423Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import astropy.units as u\n",
    "import astropy.constants as c\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.size\"] = 20"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "62f70c1f-d936-4cad-b24c-f8b5ab16b73d",
   "metadata": {},
   "source": [
    "Habitable Zone 的定义中，我们关心的变量是行星到恒星之间的距离 $R[A.U.]$ 随着行星表面温度 $T[K]$ 的关系。\n",
    "\n",
    "我们假设恒星的光度为 $L[L_\\odot]$, 其单位为太阳光度.\n",
    "\n",
    "我们先来看一下一个太阳光度转换为我们常见的功率单位 $J/s$ 是多少"
   ]
  },
  {
   "cell_type": "code",
   "id": "bdbe2d1b-6196-4a7f-a03e-335ffba3f32a",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.428804Z",
     "start_time": "2024-11-20T06:58:11.425274Z"
    }
   },
   "source": [
    "u.L_sun.in_units(u.J/u.s) # 可以用太阳常数验证一下"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.828e+26"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "85c78359-963c-4ae1-8f39-f5dda35d4153",
   "metadata": {},
   "source": [
    "Stefan-Boltzman 常数"
   ]
  },
  {
   "cell_type": "code",
   "id": "5db12d20-3d10-41c0-aedc-4af208adc773",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.500806Z",
     "start_time": "2024-11-20T06:58:11.497943Z"
    }
   },
   "source": [
    "c.sigma_sb"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<<class 'astropy.constants.codata2018.CODATA2018'> name='Stefan-Boltzmann constant' value=5.6703744191844314e-08 uncertainty=0.0 unit='W / (m2 K4)' reference='CODATA 2018'>"
      ],
      "text/latex": "$5.6703744 \\times 10^{-8} \\; \\mathrm{\\frac{W}{m^{2}\\,K^{4}}}$"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "cbacf4a9-0146-4a1e-8574-9c617933615b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 行星接收的辐射功率\n",
    "\n",
    "我们假设行星的半径为 $r[km]$，那么行星接收到的恒星辐射功率为\n",
    "\n",
    "$P_{accept} = \\dfrac{\\pi r^2}{4 \\pi R^2} L $ \n",
    "\n",
    "## 行星热辐射功率\n",
    "\n",
    "$P_{thermal} = 4 \\pi r^2 \\cdot \\sigma T^4$ \n",
    "\n",
    "## 如果行星达到平衡状态\n",
    "\n",
    "$P_{accept} = P_{thermal}  $\n",
    "\n",
    "可以得到 $R$ 和 $T$ 的问题:\n",
    "\n",
    "$R(L, T) = \\dfrac{1}{4 T^2} \\sqrt{\\dfrac{L}{\\pi \\sigma}}$\n",
    "\n",
    "或\n",
    "\n",
    "$T(L, R) = \\left( \\dfrac{L}{16 \\pi \\sigma R^2} \\right) ^\\frac{1}{4}$"
   ]
  },
  {
   "cell_type": "code",
   "id": "7461b13e-0a6e-4a0f-86d9-e7f8d386e2e7",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.508280Z",
     "start_time": "2024-11-20T06:58:11.505642Z"
    }
   },
   "source": [
    "def calculate_temperature(L: float = 1., R: float = 1.):\n",
    "    T = np.power(L * u.L_sun / (16 * np.pi * c.sigma_sb * (R * u.astronomical_unit) ** 2),1/4)\n",
    "    return T.to(u.Kelvin).value\n",
    "\n",
    "def calculate_distance(L: float = 1., T: float = 278.):\n",
    "    R = 1 / (4 * (T * u.K) ** 2) * np.sqrt(L * u.L_sun / np.pi / c.sigma_sb)\n",
    "    return R.to(u.astronomical_unit).value"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "1d9edd12-63cb-4135-a092-927e8781f8bd",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.519733Z",
     "start_time": "2024-11-20T06:58:11.517182Z"
    }
   },
   "source": [
    "# 距离太阳1AU的温度\n",
    "calculate_temperature(1., 1.)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(278.3299094150219)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "b1c988d7-0ca7-4562-bfa5-77efeb22ed70",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.532186Z",
     "start_time": "2024-11-20T06:58:11.529705Z"
    }
   },
   "source": [
    "# 太阳周围温度为278K的距离\n",
    "calculate_distance(1., 278.)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0023748573440077)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "136b2257-8550-4027-9149-097ac5df1660",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.612328Z",
     "start_time": "2024-11-20T06:58:11.548068Z"
    }
   },
   "source": [
    "R = np.logspace(-1, 1)\n",
    "T = calculate_temperature(L=1, R=R)\n",
    "plt.plot(R, T, lw=3)\n",
    "plt.xlabel(\"Distance to star [A.U.]\")\n",
    "plt.ylabel(\"Balance temperature [K]\")\n",
    "plt.hlines(273, 0, 10, colors=\"r\")\n",
    "plt.xlim(0, 10)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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bu/bMmTNYuXIlunTpAi8vLzg6OqJevXqYPHky/vjjjxKFusI4Kg3rwwmKiYiIqEK32NkKY4n/0sMkDF5+WjpHpVTg+sf9rVVFIiIiyoctdmS2/IMnstVaaLXM6ERERLZM/lP8T2m1Whw9ehRnz55FTEwMMjIysHDhQtSoUUM6JycnB2q1GnZ2dkWuhUqm5R88AeR1xzo7WOxHSkRERBWMRVLAnj17MGPGDNy7d89g/9tvv20Q7NasWYPp06fD1dUV0dHRsldfsGVODgWDXWYOgx0REZEtk90V+/3332PIkCG4e/cuRFGEl5cXCntsb9KkSfDw8EBaWhp27Nght2ib5mwk2GXkcAAFERGRLZMV7G7evIlp06YByJtS5OrVq4iNjS30fAcHBwwfPhyiKOLgwYNyirZ5jkoGOyIiIjIkK9gtXrwYarUazZo1w759+9C4ceMir+natSsAIDw8XE7RNk+hEAoMoMjIMb1uLhEREVVusoLdkSNHIAgCZs2aBQcHB7OuadCgAQDgwYMHcoomFOyOzWSLHRERkU2TFewePnwIAGjZsqXZ1+gGTGRkZMgpmlBwAAW7YomIiGybrGAnCAKA4oW0J0+eAAA8PDzkFE0o2GKXwfViiYiIbJqsYFezZk0AwJ07d8y+5tSpUwCAevXqySmagAJTm2TyGTsiIiKbJivY9ejRA6IoYsOGDWadn5ycjG+//RaCIKBXr15yiiYUbLFLz2aLHRERkS2TFeymTp0KQRBw/PhxrF+/3uS5T548wQsvvICYmBgolUr85z//kVM0wcjgCXbFEhER2TRZwa5169aYOXMmRFHExIkT8fLLL2Pbtm3S8TNnzuDHH3/EtGnT0KBBA5w4cQKCIOD9999HnTp1ZFfe1jnl64rldCdERES2Tfb6U1999RWys7OxatUqbN++Hdu3b5cGVUydOlU6T7caxaxZs/Dee+/JLZYAOBeYx44tdkRERLZM9pJigiBgxYoVOHDgAHr06AFBECCKosEXAHTs2BF79+7F119/LbvSlCf/dCecx46IiMi2WWzF+L59+6Jv375ITU1FeHg4YmNjodFo4OXlhVatWsHb29tSRdFTBaY7YbAjIiKyabKC3YQJEwAA/fv3x0svvQQAcHNzQ7du3eTXjIrkouIzdkRERPQPWcFON83Jyy+/bJHKUPFwuhMiIiLSJ+sZOx8fHwBAtWrVLFIZKh6XfKNi09liR0REZNNkBbumTZsCAO7du2eRylDx5O+KTctmsCMiIrJlsoLd6NGji7XyBFmWiyrf4Al2xRIREdk0WcFu/Pjx6N27N3bt2oUPPvhAmtqEyoZrvha7dLbYERER2TRZgydOnjyJt99+G3FxcViwYAG2bt2Kl19+GS1atEDVqlVhZ2dn8nqOnpUnf1dseo4aoihKE0QTERGRbZEV7HQTEuvcuHEDCxYsMOtaQRCgVrOFSY78gye0Yt56sc4OFpuekIiIiCoQ2QmA3a/Wk/8ZOyBvAAWDHRERkW2SlQCOHj1qqXpQCeTvigWezmXnZoXKEBERkdXJCnbdu3e3VD2oBFRKBZQKAWrtP62mHEBBRERku2SNiiXrEgSh4AAKBjsiIiKbxWBXwbnkX1aMq08QERHZLAa7Cq7g6hOcpJiIiMhWyXrGrlevXiW+VhAEHD58WE7xBCNz2bErloiIyGbJCnbHjh2DIAgmpzzJP1mu7lxOomsZbo75WuyyGOyIiIhslaxg161btyIDWnp6Om7duoWkpCQIgoDAwEDUqFFDTrGkJ/+yYqlssSMiIrJZslvszLVv3z7MmDEDCQkJCA4ORufOneUUTU/lb7FLzcq1Uk2IiIjI2sps8ERQUBBOnToFpVKJoUOHIioqqqyKrtTcHO0NXqeyK5aIiMhmlemo2OrVq+PNN99EfHw8Pv/887IsutIq0BXLFjsiIiKbVebTnXTp0gUAsHfv3rIuulIqMHiCz9gRERHZrDIPdg4ODgCA6Ojosi66UnJnVywRERE9VebB7tSpUwAAZ2fnsi66Uio4eILBjoiIyFaVabA7e/YsPvroIwiCgGeffbYsi660XDkqloiIiJ6SNd3JRx99VOQ5Wq0WiYmJCAsLw/nz56HVaiEIAt588005RdNT+UfFprDFjoiIyGbJCnYffPBBsVaQEEURSqUSn3/+Ofr27SunaHoqf1dsjlqLbLUGKqWdlWpERERE1iIr2AEwuZwYkLd0mJubGwICAtC9e3dMmTIFTZs2lVssPZU/2AF5y4qpXBnsiIiIbI2sYKfVai1VDyohN5V9gX2pWWp4uaqsUBsiIiKypjIfFUuW5WivgFJh2B3OkbFERES2icGughMEAe5Ohq12yZkcGUtERGSLZAU7hUIBpVKJq1evmn3N7du3pevIMqow2BEREREs0GJX1OAJS19HBbHFjoiIiAArdsUWZ5oUMs2DwY6IiIhghWAXHx8PAHBxcSnroistBjsiIiICLBTszG19S09Px7JlywAA9evXt0TRBAY7IiIiylOsEQz16tUzur9fv36wty84n5q+7OxsxMbGSkuKDRo0qDhFkwn5g10Kgx0REZFNKlawu3v3boF9oigiKiqqWIV26NAB77zzTrGuocKxxY6IiIiAYga7sWPHGrzesGEDBEHA4MGDUaVKlUKvEwQBjo6OqFGjBjp16oRevXpx8IQFMdgRERERUMxgt27dOoPXGzZsAAAsXLiQ679aEac7ISIiIkDmWrHz588HAPj6+lqkMlQybLEjIiIiwELBjqyrwOCJrFxotSIUCnZ3ExER2RKuFVsJeDgbBjtRBFKz1FaqDREREVmLxRdsvXv3LuLj45GZmVnksmHdunWzdPE2ydPZocC+xIycAoGPiIiIKjeLBLvr16/jk08+we7du5GSkmLWNYIgQK1mq5IlODnYQaVUIFutlfYlZuSgLri6BxERkS2RHex27tyJUaNGISsrq8gWOio9ni4OeJScJb1OzMixYm2IiIjIGmQFuwcPHmD06NHIzMxEzZo1MXv2bDg7O2PKlCkQBAEhISFISEhAWFgYNm3ahOjoaHTp0gUffPAB7OzsLPUeCEAV53zBLp0jY4mIiGyNrGC3dOlSZGRkwM3NDefPn4efnx+uXLkiHe/ZsycAYPjw4Zg3bx4mTpyIrVu3Ijg4GD/88IO8mpMBTxfD5+nYYkdERGR7ZI2KDQkJgSAIeP311+Hn52fyXCcnJ2zevBmtW7fGTz/9hF9++UVO0QXcv38f8+fPR7t27eDj4wNHR0f4+/uja9eumDdvHi5fvmzy+v3792Po0KGoVasWVCoVatWqhaFDh2L//v1m10GtVuPbb79F165d4ePjAycnJ9SvXx9Tp041CLyloUq+ARQJ6Qx2REREtkZWi51u7dhOnTpJ+/SXClOr1VAq/ylCoVBgxowZGDduHNauXYvhw4fLKV6ybNkyzJkzB+np6Qb7Hz58iIcPH+LUqVNISUnBkiVLClyr1WoxZcoUBAcHG+yPiopCVFQUdu7ciUmTJuG7776DQlF4Do6Pj0dQUBBCQ0MN9t+5cwerV6/Ghg0bsHz5ckyaNKnkb9SE/CNjEzPYFUtERGRrZLXY6YKUv7+/tM/Z2VnaTk5OLnBNs2bNAAB//fWXnKIlH3/8MWbMmIH09HQEBgbiiy++wLFjxxAeHo6QkBB88cUX6NSpU6GhbO7cuVKoa926NbZs2YILFy5gy5YtaN26NQBgzZo1eO+99wqtg0ajwdChQ6VQN2zYMOzfvx/nz5/H0qVL4evri+zsbEydOrVYLYDFUdUlX7Bjix0REZHtEWXw9vYWFQqFePbsWWlfSkqKKAiCqFAoxPPnzxe45vDhw6IgCKJKpZJTtCiKohgSEiICEAGIY8aMEXNycgo9Nzs7u8C+69evi0qlUgQgtmvXTszIyDA4np6eLrZr104EICqVSvHmzZtG7x0cHCzV4/XXXy9w/ObNm6K7u7sIQGzQoIGYm5tbrPeZnJwsAhCTk5MLPWftqTtinXf3SF8jvj1TrDKIiIjIssz5/LY0WS12jRo1ApDX3ajj5uaGOnXqAAAOHjxY4JpDhw4BAKpUqSKnaGi1Wrz22msAgJYtWyI4OBj29oVPyOvgUHAS3yVLlkhz6S1btgxOTk4Gx52dnbFs2TIAed3KixcvNnrvL7/8EgDg6emJL774osDxBg0aYM6cOQCAW7duYceOHUW9vWLzzN9ix8ETRERENkdWsOvYsSMA4Ny5cwb7Bw4cCFEU8cUXX+Do0aPS/m3btuGbb76BIAjo3LmznKJx8OBB3Lx5EwDw7rvvGjzLZw5RFLFr1y4AQOPGjdGhQwej53Xo0EEKsLt27SowV9+NGzcQEREBABgxYoRBV7S+cePGSdulEewKDp7gM3ZERES2RlawCwoKgiiK+PXXX6HRaKT9uvns0tLS0KdPH/j4+MDNzQ0jR45EVlYWFAoFZs+eLaviP//8M4C8wRoDBw6U9ickJODmzZtISEgweX1kZCSio6MBAN27dzd5ru54VFSUNGBE59SpUwXOM6Z69eoIDAwEAJw+fdpkeSWRf/BEUkYOJ4wmIiKyMbKCXY8ePTB//nyMHz8eUVFR0v7atWvj559/hoeHB0RRxJMnT5Ceng5RFKFSqfD9998X2kJmLl0rYd26deHm5oYff/wRzZs3h5eXFwIDA+Hl5YVGjRrhyy+/RHZ2doHrr169Km03btzYZFn6x3Wtc3Lu8+DBgwIjeOXydDUMdmqtiJRMLtlGRERkS2RNdyIIAubPn2/0WP/+/XHz5k1s374dV65cgVqtRsOGDTFixAjUrFlTTrHQarW4du0aAMDb2xszZ87E0qVLC5x348YNzJ49Gzt27MDevXsNnut7+PChtF2rVi2T5emP+n3w4IHBsZLcRxRFPHz4UOrizS87O9sgjJqz/q6XS8FnCOPSsuHhXPhzh0RERFS5yF4r1hQvLy9MnTrV4vdNTk6GVpu34P3ff/+N0NBQ1KhRA1988QWCgoLg6OiI0NBQvPvuuzh37hzOnDmDCRMm4Ndff5XukZqaKm27urqaLM/FxUXaTktLMzhmqfvo+/TTT/Hhhx+avFd+jvZ2cFMpkZr9Tyvdk7RsNPA1XSciIiKqPGR1xU6YMAETJkyQnncrK/rdmFlZWXB2dsbRo0cxatQoVK1aFU5OTujWrRuOHDmCli1bAsgbsHD+/HmD63SMjZjVp1KppO3MzEyDY5a6j745c+YgOTlZ+srfSlgYr3zdsfFpHBlLRERkS2QFuw0bNmDDhg1wd3e3VH3M4ujoaPB60qRJRrs1nZycsHDhQun11q1bjd4jJ8d0ANLvFs0/JYql7qNPpVLB3d3d4Msc3q4qg9dP0gs+W0hERESVl6xg5+PjAwCoVq2aRSpjLjc3N4PX/fr1K/Tc3r17S1Oh6C/3pX8PU92igGELYf7uVkvdxxLyB7v4VAY7IiIiWyIr2DVt2hQAcO/ePYtUxlwqlUoKlYDh4Ib8HB0d4e3tDQCIi4uT9usPdNAfAGGMfldo/rJKch9BEIocaFESBbpiuawYERGRTZEV7EaPHg1RFLFhwwZL1cdsujVnARjMoWeM7rj+JMa6UApAGmFbGP3jTZo0MThWkvv4+/sbDKSwFLbYERER2TZZwW78+PHo3bs3du3ahQ8++KBMJ8Tt1q2btK2/pFl+KSkpiI+PBwCDaVYCAgLg5+cHADh+/LjJsk6cOCFdX7duXYNjXbp0kbZN3ScmJgY3btwAANmrbhTGu8DgCQY7IiIiWyJrupOTJ0/i7bffRlxcHBYsWICtW7fi5ZdfRosWLVC1alXY2dmZvF4/nBXX8OHD8dFHHwHIG/E6fPhwo+ft2LFDCpxdu3aV9guCgCFDhmDVqlW4du0azp07Z3TS5HPnzkktbUOGDIEgCAbHAwMD0aRJE0RERGDbtm346quvjC4rtn79eml76NChxXuzZio4eIJdsURERLZEEGU0sykUigJBx+yCBQFqtbyVEYKCgrB//34oFAocPHgQvXv3NjgeExOD9u3b4+HDh3BwcMCdO3cMWu1u3LiBpk2bQqPRoF27djhx4oTBaNXMzEx069YNYWFhUCqVuHr1Kho2bFigHmvXrsXEiRMBANOmTcPy5csNjt++fRtt2rRBSkoKGjRogIiIiGKtbZuSkgIPDw8kJyebHCF7ITIBI747K712cbDDlY+eN7scIiIishxzP78tSVZXLJC3ikJJv+RasmQJqlSpAq1Wi4EDB2LOnDk4efIkwsLCsHLlSinUAcCCBQsKrHgRGBgorVkbFhaGzp07Y+vWrQgLC8PWrVvRuXNnhIWFAchb/9ZYqAOAsWPHSt2rK1aswIsvvogDBw7gwoULWL58OTp16oSUlBQoFAosXbq0WKGuOPJ3xabnaJCZY/r5QyIiIqo8ZLXYFfVsWlG6d+8u63oAOHXqFF588UU8fvzY6HFBEDB37lwsWLDA6HGtVovJkydj7dq1hZYxceJErF69GgpF4Tk4Pj4eQUFBBlOq6FOpVFi+fDkmTZpk4t0YZ27iT8tW45n5Bwz2HZ/dA3W8LD9Qg4iIiEyzRoudrGBXXjx58gTLli3Dzp07ERkZiZycHNSoUQM9evTA9OnT0bp16yLvsW/fPqxevRqhoaGIj4+Ht7c32rdvj6lTp6J///5m1UOtVuP777/Hjz/+iIiICKSnp8PPzw+9e/fGzJkzDUbyFkdxfjGazfsd6XqtdNumdsSzAZ4lKpeIiIhKjsGOjCrOL0avL4/hTvw/EyEvG9kag1r6lXYViYiIKJ8K+YwdlS++7oYjYx+nZBVyJhEREVU2FnuKPzk5Gdu3b8fZs2cRExODjIwMrFu3DnXq1JHOiY6ORlJSEhwdHVGvXj1LFU16qrkbrqMby0mKiYiIbIZFgt3y5csxd+5caa1UURQhCILB2qgAcOzYMYwePRqOjo54+PAhPD357Jel+bqxxY6IiMhWye6KnT9/PmbOnInU1FQ4ODigbdu2hZ77yiuvoHr16sjOzsYvv/wit2gyokCLXQpb7IiIiGyFrGD3xx9/4OOPPwaQt25sTEwMLly4UHhhCgVeeukliKKIQ4cOySmaCuGbL9g9TmWLHRERka2QFeyWL18OURTRsWNHbNy4ER4eHkVe07FjRwDA33//LadoKkS1fF2xbLEjIiKyHbKC3YkTJyAIAt544w2zr6lbty4AICoqSk7RVIj8XbFp2WqkZctbuo2IiIgqBlnB7tGjRwCARo0amX2No2Ne8MjOZktSacg/3QnAARRERES2Qlawc3DIW5s0KSnJ7Gt0S39VqVJFTtFUCGcHJTyc7A32xSQz2BEREdkCWcGudu3aAICbN2+afc2RI0cAFK+Vj4qnhodhd2xUUqaVakJERERlSVaw6927N0RRxLfffmvW+VFRUVi9ejUEQUC/fv3kFE0m1KziZPA6msGOiIjIJsgKdm+88Qbs7e3x119/YcGCBSbPvX79Op5//nkkJyfD2dkZU6dOlVM0meDHYEdERGSTZK08Ub9+fSxcuBDvvPMOPvjgA+zduxfDhg2Tjv/888+wt7fH6dOncfDgQWi1WgiCgCVLlsDHx0d25cm4gsGOz9gRERHZAtlLir399tsQRRHvvfceLly4gNDQUAiCAAD46KOPpPNEUYSdnR2+/PJLTJw4UW6xZIJfFcNn7NhiR0REZBtkLykGALNnz8bFixcxfvx4eHt7QxRFgy93d3eMHDkS4eHhmDlzpiWKJBPyP2MXlZQJURStVBsiIiIqK7Jb7HSaNGmC4OBgAMD9+/cRGxsLjUYDLy8v1KtXDwqFRTIkmSF/V2y2WouE9Bx4uRac446IiIgqD4sFO321a9eWpkKhsufrpoJCALR6jXTRSVkMdkRERJUcm9EqIaWdAtXdOZcdERGRrbFoi92ff/6JkJAQ/P3330hISAAAeHp64plnnkGfPn3Qtm1bSxZHJvhVcUK03ooTDxMzrFgbIiIiKgsWCXZ//vknXn/9dYSGhhZ6zv/93/+hXbt2WLFiBdq1a2eJYskEf09nhN1LlF4/TGSLHRERUWUnuyt2+/bt6NSpE0JDQ6VRsPb29qhWrRqqVasGe3t7aX9oaCg6d+6Mn3/+2RJ1JxP8PZ0NXt9PYIsdERFRZScr2F2/fh2vvvoqcnJyYGdnh9deew2hoaFIT09HdHQ0oqOjkZ6ejrCwMLz22mtQKpXIzc3FmDFjcO3aNUu9BzKiTr5gd+9JupVqQkRERGVFVrBbtGgRsrOz4ejoiIMHD2LFihVo27Yt7OzspHPs7OzQpk0brFixAocOHYKjoyNycnLw+eefy648Fa62l2Gwe5CYCa2Wc9kRERFVZrKCXUhICARBwKxZs9CjR48iz+/evTtmzZoFURQREhIip2gqQu18LXY5ai0ep3JpMSIiospMVrCLi4sDAAQFBZl9zYABAwyupdLh66aCSmn4473/hM/ZERERVWaygp2Pjw8AwNHRsYgz/6FS5U2S6+3tLadoKoIgCAVa7TiAgoiIqHKTFew6d+4MACanOcnvwoULAIAuXbrIKZrMUMeLwY6IiMiWyAp2b731Fuzs7PDJJ5+Y1bUaGxuLTz/9FPb29njzzTflFE1m4JQnREREtkVWsGvfvj2+++47xMbG4rnnnsPOnTuh1WoLnKfVarFr1y507NgRcXFxWLVqFZ599lk5RZMZ8k95cpfP2BEREVVqslaemDBhAgCgadOm+OuvvzB8+HBUrVoVrVu3hq+vLwRBwOPHj3Hx4kVpibGWLVvi1KlTOHXqlNF7CoKA4OBgOdWip+p4uxi8joxLgyiKEATBSjUiIiKi0iSIoljiyc0UCoVBSNDdKn9wKGx/frrQodFoSlqlSiklJQUeHh5ITk6Gu7u72dc9SMhA18+PGuwLndsHPm4qS1eRiIiI8inp57ccslrsateuzdafcsyvihMclArkqP/pHr8Tl8ZgR0REVEnJCnZ37961UDWoNNgpBNTzdsG1mFRp3+24dDxXz8uKtSIiIqLSImvwBJV/9X1cDV7fjkuzUk2IiIiotDHYVXL1fQwHUNxhsCMiIqq0GOwquXoFWuzSrVQTIiIiKm2ynrHLLzU1FZGRkUhNTTVrZGu3bt0sWTwZkb8r9kFiBrJyNXC0t7NSjYiIiKi0yA52oiji+++/x6pVq3Dp0iWzrxMEAWq1Wm7xVIR6+bpiRRG49yQDjaq7WalGREREVFpkBbvc3Fy88MIL+P333wH8M18dlR8uKiWquzsiJiVL2nczNpXBjoiIqBKSFey++uor7N+/HwBQp04djB07Fi1btkSVKlWgUPDxvfKiYTVXg2B3PSYVA1tYsUJERERUKmQFu02bNgEAOnbsiJCQEDg5OVmkUmRZTWq44+TNeOl1xKNUE2cTERFRRSWrWS0yMhKCIGDOnDkMdeVYo2qG3a7XH6dYqSZERERUmmQFu6pVqwIAatWqZZHKUOloXMMw2D1IyERaNgeuEBERVTaygl3Lli0BcGmx8q6BryvsFIZr+l6PYXcsERFRZSMr2L3xxhsQRRHBwcGWqg+VApXSDvW8Dac9uRbD7lgiIqLKRlawCwoKwvTp07F37168/fbbZk1KTNbRuIa7wetrHEBBRERU6cieoPibb75BnTp18N5772H79u0YNmwYAgMD4ezsXOS1Y8aMkVs8malxdTf89tc/r9kVS0REVPnIDnaZmZlITEyEvb09Hjx4gG+++cas6wRBYLArQ43zTUgcEZMCURQhCEIhVxAREVFFIyvYZWRkoF+/fjh79iwArjxRnjXJ1xWbmqXG3ScZCMj37B0RERFVXLKC3ddff40zZ84AADp06IApU6Zw5YlyqoaHI7xdHRCfliPtu/QwicGOiIioEpEV7H788UcIgoD+/ftj9+7dDHPlmCAIaFGrCo5ci5X2XXqYjCGtalqxVkRERGRJspKYbv66mTNnMtRVAC1qeRi8/vthspVqQkRERKXBIitPeHt7W6QyVLryB7vL0cnQaPlcJBERUWUhK9i1b98eAHDjxg2LVIZKV/OaVQxeZ+RocCs2zTqVISIiIouTFexmzpwJAFi+fDlHxFYAPm4q+Hk4Guy79DDJOpUhIiIii5MV7Hr27ImFCxfi9OnTeOWVV5CUlGShalFpaVGrisHrS3zOjoiIqNKQNSr2o48+AgA8++yz+Pnnn7Fv3z707dvX7JUn5s2bJ6d4KoEW/h74/UqM9PrigyTrVYaIiIgsShBl9KEqFAqDlQuKu5IB15Y1T0pKCjw8PJCcnAx3d/eiLzDhzO14/Pv789JrO4WAS/P7wUUlexESIiIi0mPJz29zyZ6jRBRF6Sv/66K+qOy18q8CpeKf8K3Rigi/n2S9ChEREZHFyAp2Wq1W1heVPWcHJZrVNJz2JPRugpVqQ0RERJbEWYVt0LN1qxq8ZrAjIiKqHBjsbFC7up4Gr8PvJyFXwxZUIiKiiq5SBrt3330XgiBIX8eOHSvymv3792Po0KGoVasWVCoVatWqhaFDh2L//v1ml6tWq/Htt9+ia9eu8PHxgZOTE+rXr4+pU6fiypUrMt6RZbWrY9hil5mrwZXoFCvVhoiIiCzFYsHu8OHDePXVV9GgQQO4urpCqVTi6tWrBuecOHECK1euxObNmy1VbAEXL17E119/bfb5Wq0WkyZNQlBQEHbu3ImoqCjk5OQgKioKO3fuRFBQECZPnlzkM4Hx8fHo1KkTXnvtNZw6dQrx8fHIysrCnTt3sHr1arRt2xZr1qyR+/YswstVhfo+Lgb7wtgdS0REVOHJDnYZGRl46aWX0K9fP/z444+4c+cOMjIyjI56tbOzwxtvvIGxY8fi5s2bcosuQKvVYsqUKVCr1fD19TXrmrlz5yI4OBgA0Lp1a2zZsgUXLlzAli1b0Lp1awDAmjVr8N577xV6D41Gg6FDhyI0NBQAMGzYMOzfvx/nz5/H0qVL4evri+zsbEydOrVYLYCl6dkAw+7YC5EMdkRERBWd7GA3YsQI/PrrrxBFEe3bt8fbb79d6LmdO3fGM888AwD45Zdf5BZdwNKlSxEaGorGjRtj4sSJRZ5/48YNfPnllwCAdu3aSStotG/fHq+88gpOnTqFdu3aAQC++OIL3Lp1y+h9NmzYgFOnTgEAXn/9dfzyyy94/vnn8eyzz2L69Ok4ffo03N3dodVqMWPGDKjVagu945JrV8cw2J278wRqPmdHRERUockKdr/88gv27dsHAFi9ejXOnTuHzz//3OQ1w4YNgyiKOH78uJyiC7h//z7ef/99AMC3334LBweHIq9ZsmSJFLKWLVsGJycng+POzs5YtmwZgLzn5xYvXmz0Prpw6OnpiS+++KLA8QYNGmDOnDkAgFu3bmHHjh1mvqvS07mBt8HrlCw1LkVxeTEiIqKKTFaw27BhAwBg9OjRmDRpklnXtG3bFgAQEREhp+gCpk2bhrS0NIwdOxbdu3cv8nxRFLFr1y4AQOPGjdGhQwej53Xo0AGNGjUCAOzatatAF/ONGzek9zJixIhCl1IbN26ctF0egl11D0cEVnM12HfqZryVakNERESWICvYhYWFQRAEvPzyy2ZfU6NGDQBAXFycnKINbNu2DXv27IGnp6fUelaUyMhIREdHA0CRQVB3PCoqCnfv3jU4puuCLeo+1atXR2BgIADg9OnTZtWxtHVp4GPw+uRNy/1MiIiIqOzJCnZPnjwBAPj5+ZlfoCKvSEutPJGUlISZM2cCABYtWgRvb+8irsijP2K3cePGJs/VP56/pbEk93nw4AHS09PNqmdp6hpo+L36834SUrNyrVQbIiIikktWsPPwyFuaStfyZY7IyEgAMDuAFeWdd95BTEwMOnfubNaACZ2HDx9K27Vq1TJ5rr+/v7T94MED2fcRRdHgOmt5LsATDnb//ApotCLO3eHoWCIioopKVrDTdS3+9ddfZl+zc+dOAJCmEpHj5MmTWLNmDZRKJb799lsIglD0RU+lpqZK266uribOBFxc/pnzLS0trVTuoy87OxspKSkGX6XB2UGJtvkmK2Z3LBERUcUlK9gNGDAAoihi2bJlyMrKKvL8kydP4qeffoIgCBg0aJCcopGTk4MpU6ZAFEW8+eab0jQq5tKvb1EjaFUqlbSdmZlZKvfR9+mnn8LDw0P60m8xtLT83bEnbjDYERERVVSygt20adPg6emJx48f48UXX0RCgvFuPLVaje+//x4DBw6EVquFv7+/wSjRkvjkk09w7do11K5dG/Pnzy/29Y6OjtJ2Tk6OyXOzs7Ol7fxToljqPvrmzJmD5ORk6St/968ldWtoOIDi7pMM3IotvDWRiIiIyi+lnIvd3d2xdetWBAUFYf/+/fD39zcYGfrOO+8gJycHYWFhSE5OhiiKcHR0xLZt22Bvb1/icq9du4ZPP/0UQN78c/pdnOZyc3OTtk11iwIwGOiQv7s1/330g15x7qNPpVIZtO6VpqY13FHNXYXHKf+EzoNXY9DAt0GZlE9ERESWIyvYAUDv3r1x5MgRjB49Gvfu3cPvv/8uPeumWz5LN/ebv78/tm3bhmeffVZWmYsXL0ZOTg7q1auHjIwM/PTTTwXOuXz5srR95MgRxMTEAAAGDRoEFxcXg4EORQ1k0G8xy98tmv8+pgaF6O4jCEKRAy3KikIhoG/Tath87r607+CVx3i9B4MdERFRRSM72AF5S4XdvHkTP/30E3bv3o2wsDDExsZCo9HAy8sLrVu3xuDBgzF27FizVoQoiq5L886dOxg5cmSR5y9YsEDajoyMhIuLC5o2bSrtu3btmsnr9Y83adLE4Fj++7Rq1arI+/j7+5eolbG09Gta3SDYXXyQhMcpWajmXnjrIxEREZU/Fgl2AKBUKjF69GiMHj3aUrcsVQEBAfDz80N0dHSRy5udOHECAFCzZk3UrVvX4FiXLl2k7ePHj+OVV14xeo+YmBjcuHEDQF4QLk861POCm6MSqVn/rGF76OpjjO5Qx4q1IiIiouKSNXjCWtavXw9RFE1+6Q+oOHr0qLRfF8wEQcCQIUMA5LWknTt3zmhZ586dk1rahgwZUmBKlcDAQKkVb9u2bcjIyCi0zjpDhw4t0fsuLQ5KBXo19jXYd/DqYyvVhoiIiEpKVrALCAhA/fr1cevWLbOvuX//PurVq4f69evLKdoiZs2aBTs7OwDA9OnTC0xBkpmZienTpwPIa5GcNWuW0fu8/fbbAICEhAS88847BY7fvn1bGuzRoEGDchfsgLzuWH1nb8cjhatQEBERVSiygt29e/dw9+7dIqf50Jebm4u7d+8WWHPVGgIDAzF79mwAeevedu7cGVu3bkVYWBi2bt2Kzp07IywsDAAwe/ZsNGzY0Oh9xo4dK3WvrlixAi+++CIOHDiACxcuYPny5ejUqRNSUlKgUCiwdOlSKJUW6wG3mO6NfAxWocjViDgcwVY7IiKiiqT8JYwytnDhQsTGxmLt2rUIDw83+ozcxIkT8fHHHxd6Dzs7O+zcuRNBQUEIDQ3FL7/8gl9++cXgHJVKheXLl6N///4Wfw+W4KpSoktDbxy5Fivt2xkejaGty8foXSIiIipamT9jl5ycDABwdnYu66KNUigUCA4Oxt69ezFkyBD4+fnBwcEBfn5+GDJkCPbt24c1a9ZAoTD9rfL29saZM2ewcuVKdOnSBV5eXnB0dES9evUwefJk/PHHH5g0aVIZvauSGdzSz+D1yZtxiEvNLuRsIiIiKm8EUTfJXAkoFAoIgoC///7bYNoPU9566y0sWbIETZs2NZhrjgqXkpICDw8PJCcnw93dvdTKychRo93HIcjI0Uj73h/YFBO7BJRamURERJVVWX1+6ytWV2yvXr2M7h8/fnyR87JlZ2fjzp07iI2NhSAI6NevX3GKpjLg7KDE882q49fwKGnfzvAoBjsiIqIKoljB7tixYxAEAfqNfKIoIjQ0tFiF1qtXD3PmzCnWNVQ2Xmhd0yDY/R2VjFuxqWjg62biKiIiIioPihXsunXrZjCP2/HjxyEIAtq2bWuyxU4QBDg6OqJGjRro1KkTXnnllXK18gL9o1N9L/i4qQyerdsRHoXZ/2psxVoRERGROYrdYqdPN6Bg/fr1Zj9jR+Wb0k6BIS39sOZUpLRvZ3g0/tu3ERQKwcSVREREZG2yRsWOGTMGY8aMQdWqVS1VHyoHXmhd0+B1VFImjt+Is1JtiIiIyFyy5rHTXyaLKo9mfu5oWsMdVx+lSPs2nbuHnvmWHSMiIqLypUKuFUulSxAEvNqxjsG+o9dj8SDB+Dq4REREVD4w2JFRQ1r5wU31T4OuKAKbz9+zYo2IiIioKAx2ZJSzgxLD2xouJ7Yt9AGycjWFXEFERETWxmBHhcrfHZuYkYt9fz+yUm2IiIioKAx2VKj6Pq7o3MDLYN+GM3chYxU6IiIiKkUMdmTSqx0MW+3+epiMs3eeWKk2REREZAqDHZnUp0k11KrqZLBvxdFbVqoNERERmcJgRyYp7RSY2r2+wb7Tt54g/H6ilWpEREREhWGwoyK91LYWfNxUBvtWHL1tpdoQERFRYSwe7B4+fIiwsDCcOHECmZmZlr49WYGjvR0mdw0w2BcS8RjXYlIKuYKIiIiswSLBLjU1Fe+//z78/f1Rp04dPPfcc+jZsyciIyMNzvvpp58wYsQITJ482RLFUhka9VwdeDjZG+xbdYytdkREROWJ7GB38+ZNtGnTBp988gmioqIgimKh02F06NABv/76K9auXYtTp07JLZrKkItKifGd6xrs++2vaNx4nGqdChEREVEBsoJdVlYWBgwYgNu3b8PZ2RnvvPMO9uzZU+j5devWRc+ePQEAu3fvllM0WcG4TnXh4mAnvdaKwGf7r1mxRkRERKRPVrBbtWoVbt26BRcXF5w8eRKfffYZgoKCTF7Tv39/iKKIs2fPyimarKCKswMmdjF81u7ItVicuR1vpRoRERGRPlnB7tdff4UgCJg5cyZatWpl1jUtW7YEkNeFSxXPlO714e3qYLDvk30R0Gq5GgUREZG1yQp2ERERAIB+/fqZfY2XV94SVUlJSXKKJitxVSkxq0+gwb7LUSnY/Ve0lWpEREREOrKCXVpaGgDA1dXV7Guys7MBAPb29kWcSeXVK+39Ud/HxWDfFweuIytXY6UaERERESAz2Ola3+7evWv2NVeuXAEAVK9eXU7RZEVKOwX+17+Jwb6opEwEn4os5AoiIiIqC7KCXZs2bQAAJ06cMPuajRs3QhAEdOzYUU7RZGV9mvji2QBPg33LjtzEvSfpVqoRERERyQp2L774IkRRxOrVq3H//v0iz1+yZIkUAkeOHCmnaLIyQRDw3oAmEIR/9mXlavHezsuFzmNIREREpUtWsHv11VfRokULZGVloUePHti/f7/Bh7ogCBBFEaGhoRg1ahT++9//QhAEdO3aFf3795ddebKuFrWqYGzHugb7Tt6Mx47wKOtUiIiIyMYJoszmlfv376NLly54+PAhBEGAs7MzMjIyAADe3t5ITU2VBkyIooj69evj9OnT8PX1lV97G5GSkgIPDw8kJyfD3d3d2tUxkJatRt+vj+NRcpa0r6qzPQ7/twc8XRxMXElERFS5WePzW/aSYrVr18bFixcxcuRIKBQKpKenS8uKxcXFISsrS2rFGzFiBC5cuMBQV4m4qpT4aMgzBvsSM3Lx8d6rVqoRERGR7ZLdYqfv3r172Lt3L8LCwhAbGwuNRgMvLy+0bt0agwYNQmBgYNE3oQLKc4udzmub/8D+yzEG+9aNb4+ejRjiiYjINlnj89uiwY5KR0UIdrEpWej99XGkZqmlfd6uDtg3syt83RytWDMiIiLrqJBdsUQA4OvuiDn55raLT8vBW1v/4nJjREREZYTBjixm5LP+6NPEsOv11K14fHfijpVqREREZFtkBbszZ87Azs4OTk5OiIoqeoqLqKgoODo6QqlU4o8//pBTNJVDgiDg8xdbopq7ymD/VwevI/x+opVqRUREZDtkBbuffvoJoihi4MCBqFmzZpHn16xZE4MGDYJWq8WPP/4op2gqpzxdHLDk5dYGExertSKmbwlHSlau9SpGRERkA2QFu1OnTkEQhGJNNjxgwAAAxVuGjCqWjvW9ML1nA4N9DxMz8dbWv6Dh83ZERESlRlawu337NgCgadOmZl/TuHFjAMCtW7fkFE3l3IzeDdG+blWDfSERj/H579esVCMiIqLKT1awy8rKW23A0dH86SxUqrznr9LTuVh8Zaa0U2DJK61RxdneYP93J+5gW9gDK9WKiIiocpMV7Dw9PQHkLStmrocPHwIAqlSpIqdoqgBqVnHCqlFtoVQIBvvn7vgb5+48sVKtiIiIKi+lnIubNm2K2NhY7N69G4MHDzbrmp07dwIAGjVqJKdo25SeDtjZWbsWxdKxuiM+e74e3t95xWD/rLWnsXVyB9TxdrFSzYiIiEqZFXonZQW7oKAgHD16FBs3bsTYsWPRtWtXk+efOHECmzZtgiAIGDhwoJyibZOfn7VrUCIvPv0qYGEZV4SIiKiSk9UVO3XqVHh7e0Oj0SAoKAjLly+XnrvTl5WVhaVLl2LAgAFQq9WoWrUqXnvtNTlFExEREVE+steKDQkJQVBQEDQaDQDAxcUFbdu2RY0aNQAAjx49QlhYGDIyMiCKIpRKJfbu3Yu+ffvKr72NkNaai44ut2vFmiMtW41R35/DjcdpBvtb1PJA8Lj2cFXJakAmIiIqV1JSUuDh51ema8XKDnYAcPToUbz66quIjo7Ou6lg+LC8roiaNWti06ZN6NGjh9wibYo1FhEuLVFJmXhp1RlEJxu27D5b1xPrJ7SHswPDHRERVQ7W+Py2SLADgOzsbGzcuBF79uxBeHg44uPjAQDe3t5o06YNBg0ahNGjR0vTnZD5KlOwA4C78ekY8d1ZxKZmG+zvVN8La8e1h6N9xRogQkREZEyFDnZUeipbsAOAW7FpeGX1WcSn5Rjs79HIB9+ObstwR0REFZ41Pr9lDZ4gKqkGvq7YPOm5AhMYH7sehzFrLyA5k+vKEhERFReDHVlN4+ru2DzxObg5Gj5XdyEyASO+PYtHyZlWqhkREVHFZPGu2JSUFKSmpkqjZE2pXbu2JYuutCpjV6y+P+8nYmzwBaRmqw32+3k4YsOEZ9GwmpuVakZERFRyFfYZu0OHDmHlypU4deoUEhISzCtYEKBWq4s+kSp9sAOAK9HJGLcuFHH5BlR4ONkjeGw7tKvraaWaERERlUyFfMZuxowZeP7557F79248efIEoiia/UWk08zPA7++1gn1fAyXGEvOzMWoNefx++UYK9WMiIio4pA1adiPP/6I5cuXAwAcHR3xwgsvoG3btvD09IRCwcf3qHj8PZ2x/T+dMGF9KC4+SJL2Z6u1+M/mPzCtZ3281bcR7BRC4TchIiKyYbK6Yrt3746TJ0/C398fR44cQf369S1ZN3rKFrpi9WXkqPHGj+E4ci22wLEuDbyxdGRreLo4WKFmRERE5qtwXbGXLl2CIAiYP38+Qx1ZjLODEqtfbYsR7WoVOHbqVjwGLj1p0KJHREREeWQFu9zcvLnGWrdubZHKEOko7RRYNLwF5g9qCmW+rtfo5CyM+PYsfjh/j89qEhER6ZEV7OrWrQsASEtLM30iUQkIgoDxnQPw05QO8HUzXIouR6PF3B2X8d9tfyEli5MZExERATKD3bBhwwAAhw8ftkhliIxpV9cTe2Z0wXMBBac8+TU8Cs8vPoGTN+OsUDMiIqLyRdbgieTkZLRq1QqJiYk4d+4cGjdubMm60VO2NniiMGqNFp8fuI7VJ+4YPT66Q23M6d8ELipZg72JiIgsosINnvDw8MCBAwdQrVo1dOrUCStXrkRiYqKl6kZkQGmnwP8FNcHKUW3gaiS8bT53H/2/OYnzd55YoXZERETWJ6vFrl69egCAjIwMxMbGQhAECIIAb29vODs7my5YEHD79u2SFm1T2GJX0MPEDLz7yyWcvlUwxAkCML5TAN55vhEc7e2sUDsiIqIKuKSYnEmIBUEwaz1ZYrArjFYr4ofz9/DJvmvIzC34u1SrqhPeG9AU/2pWDYLASY2JiKhsWePzW9bDSGPHjrVUPYiKTaEQ8GrHuuja0Aezt/+F0LuGjwE8TMzEfzb/ga4NvTF/UFM08HWzUk2JiIjKhqwWOyobbLErmkYrYt3pSHx+4Dpy1NoCx5UKAWM71cXMPg3h7mhvhRoSEZGtqXCDJ6wtLCwMH330Efr164datWpBpVLB1dUVgYGBGD9+PE6dOlWs++3fvx9Dhw6V7lWrVi0MHToU+/fvN/searUa3377Lbp27QofHx84OTmhfv36mDp1Kq5cuVLct0hmslMImNS1HvbN6IJnjUyLotaKCD4ViV5fHsO2sAfQavn/GSIiqnwqbItdt27dcPLkySLPGzNmDL7//ns4OBS+tqhWq8WUKVMQHBxc6DmTJk3Cd999Z/K5wvj4eAQFBSE0NNTocZVKheXLl2PSpElF1lsfW+yKRxRF/HbpET7ZG4GYlCyj57Ss5YH/9muErg29+fwdERGVCrbYFUN0dDQAwM/PDzNnzsT27dtx4cIFnD17Fl9//TVq1qwJANi4cSPGjRtn8l5z586VQl3r1q2xZcsWXLhwAVu2bJGWS1uzZg3ee++9Qu+h0WgwdOhQKdQNGzYM+/fvx/nz57F06VL4+voiOzsbU6dOLVYLIBWfIAgY3NIPh//bHdN61oeDXcFf878eJmPM2gsY8d1ZnLkdb4VaEhERWZ5FW+wSExPx119/IT4+HpmZmUWu4zlmzJgSlzVw4ECMGTMGw4cPh51dwSkt4uPj0blzZ9y4cQMAcPz4cXTr1q3AeTdu3ECzZs2gVqvRrl07nDhxAk5OTtLxjIwMdO/eHWFhYVAqlYiIiECDBg0K3Gft2rWYOHEiAOD111/HihUrDI7funULbdu2RUpKCho0aICIiAgoleaNXWGLnTz3nqRjwZ6rCImILfScjvW88N9+gWhXt2A3LhERUUlUuOlOdI4dO4b58+cX65k2QRCgVqvlFm3Snj17MGjQIADA9OnTsXTp0gLnvP7661i1ahUA4OzZs+jQoUOBc86dO4eOHTtK5+cPbQDQtGlTREREwNPTEw8ePDA6j99nn32GOXPmAAC2bduGl156yaz3wWBnGUevx+Kj364iMj690HO6Bfrgrb6BaOVfpewqRkRElVKF7IpdtWoV+vTpg1OnTkEUxWJ9lbaePXtK28YmQxZFEbt27QIANG7c2GioA4AOHTqgUaNGAIBdu3YVqPuNGzcQEREBABgxYkShkzPrdwnv2LHD/DdCFtGzkS8OvtkNnw1rjppVnIyec+JGHF5YcRoT1ofi/J0nZfJ7SkREZCmygl1ERARmzJgBURTRvHlz7Ny5E3v37gXwz8oSoaGhWLVqFdq0aQMA6NKlC65cuYI7d4yv92lJ2dnZ0rax7trIyEjpWb3u3bubvJfueFRUFO7evWtwTL+l0tR9qlevjsDAQADA6dOnTVeeSoW9nQKvPFsbR9/ugQUvPIPq7o5GzztyLRYvrz6HwctPY2d4FHI1BadQISIiKm9kBbtly5ZBo9HA29sbJ0+exODBg1G7dm3peEBAANq2bYupU6ciNDQUs2fPxqlTpzB9+nTUqVNHduWLcvz4cWm7SZMmBY5fvXpV2m7cuLHJe+kf17XOybnPgwcPkJ5eeJcglS4HpQKvdqiDY7N7YP6gpvB2VRk97++oZMzaehFdFx3FymO3kJSRU8Y1JSIiMp+sYHf8+HEIgoAZM2bAzc30rP6CIGDRokXo1asXjh49irVr18opukharRafffaZ9HrEiBEFznn48KG0XatWLZP38/f3l7YfPHgg+z6iKBpcpy87OxspKSkGX1Q6HO3tML5zAE6+0xP/F9QYni7Gp8WJScnC579fR8dPj2Derssmn9MjIiKyFlnBThdMdN2sAAzmBMvNzS1wzZQpUyCKIjZv3iyn6CItXrwYFy5cAJA39Ujbtm0LnJOamiptu7q6mryfi4uLtJ2WllYq99H59NNP4eHhIX3ph0oqHU4OdpjSrT5OvtMT8wc1hb+n8WfwMnM12Hj2Hnp9dQwT14fi0NXHULObloiIyglZwS4rK2/yVz8/P2mffnBJTEwscI1uqhD97ktLO378OP73v/8BAHx9faVRr/np6g/A5ATGQN7kwjqZmZmlch+dOXPmIDk5WfrK30JIpcdFpcT4zgE49nZPfDu6LdrXrWr0PFEEDl+LxeSNYej42RF8uj8Ct+OMB3UiIqKyYt5EaoXw9PREbGyswbNiPj4+UqvdjRs34Ovra3BNfHzeZLBJSUlyii7UlStXMHToUKjVajg6OuLnn38uUAcdR8d/HpzPyTH97JT+QAz9ee6M3Uf/dXHuo6NSqQwCIJU9O4WA55+pjuefqY5LD5MQfCoSey89gtrIUmRxqdn47vgdfHf8DtrVqYoR7f0xoHkNuKhk/fUiIiIqNlktdrqBADdv3pT2OTs7o2HDhgCA3bt3F7hGN82Hj4+PnKKNioyMRL9+/ZCYmAg7Ozv89NNPRicl1tF/LrCwblEd/fCav7vVUveh8qlFrSr45pXWOPluT/yne324OxYe2MLuJeKd7Zfw7MIQvLv9Ev64l8gpU4iIqMzICnZdunSBKIoF1mwdNmwYRFHE0qVLsW7dOqSnpyM2Nhaff/451qxZA0EQ0KtXL1kVzy86Ohp9+vRBdHQ0BEHA2rVrMWTIEJPX6A90KGwgg45+d2j+Z95Kch9BEIocaEHlSw0PJ/yvf2OcndMbnw1rjja1qxR6bnqOBlvDHmD4qjPo/dVxfHXwOq7FpDDkERFRqZIV7AYOHAgA2Llzp8FzZv/973/h6emJ3NxcTJo0Ce7u7qhRowbmzJkDjUYDR0dH6Rk4S4iPj0ffvn2lufGWLVtm1nJlTZs2lbavXbtm8lz94/mnTinJffz9/Q2eR6SKw0WlxCvP1savr3fGoTe7YXLXAHgVMpoWAO7Ep2PZkVt4fslJ9Pn6OL4+eB3XY1IZ8oiIyOJkBbvnnnsO69atw6JFiwwGSnh5eeHAgQOoW7dugdUmfH19sWPHDqPzypVEcnIy/vWvf0mDMT777DNMmzbNrGsDAgKkgR/6c94Zc+LECQBAzZo1UbduXYNjXbp0kbZN3ScmJkZau7Zz585m1ZHKt4bV3DB3QFOcndMb345ui16NfaEQCj//dlw6lh65hX8tOZEX8g7dwI3HqYVfQEREVAwWWSu2MLm5uThy5AiuXLkCtVqNhg0b4l//+lehS24VV0ZGBvr16yet4jB37lx8/PHHxboH14olS4tJzsIvfz7Ez2EPcPdJhlnXNPB1xYDmNfCvZtXRpIabwbRBRERUMVnj87tUg11pysnJwaBBg3Dw4EEAwMyZM7FkyZJi3+fGjRto2rQpNBoN2rVrhxMnThiMVs3MzES3bt0QFhYGpVKJq1evSoND9K1duxYTJ04EAEybNg3Lly83OH779m20adMGKSkpaNCgASIiIqBUmjdqksGuYhJFERcfJGHvpUfY9/cjRCdnFX0RgOrujujZ2Ac9G/micwNvjq4lIqqgGOyKYfjw4fj1118BAL169cKSJUtMtnI4ODhI67TmN2fOHGmVitatW+Pdd99F/fr1cfv2bSxatAjh4eHSeZ988onRe2g0GnTv3l1qPRw+fDgmT56MqlWr4sKFC1iwYAFiY2OhUCiwZ88e9O/f3+z3ymBX8Wm1Ii4+/CfkPTIz5DnYKfBsgCd6NvZFz0Y+qOfDkdRERBUFg10xFLerqk6dOrh7967RY1qtFpMnTza5zNnEiROxevVqKBSFP5YYHx+PoKAghIaGGj2uUqmwfPlyTJo0qVh1Z7CrXLRaEeEPkrDv7+KFPACo6+WMHo180bOxL54L8ISjvV0p1pSIiOQot8FON3DA0kzNMVcUSwY7nX379mH16tUIDQ1FfHw8vL290b59e0ydOtXsFja1Wo3vv/8eP/74IyIiIpCeng4/Pz/07t0bM2fORLNmzYpVb4DBrjLLC3mJ2HspBociYvAgwfhqJMY42dvh2QBPdKjnhY71vfCMnzuUdrLGQxERkQWV22CnUCgs/jC3IAhQq9UWvWdlxWBnG0RRxO24dBy9Fouj12NxITLB6EoXhXFVKdG+blV0rO+FjvW80dTPHXamhugSEVGpKtfBzuIFCwI0Go3F71sZMdjZptSsXJy+FY8j12Jx9Hoc4lKzi75Ij5ujEs89bdHrUM8LTWu4Q8GgR0RUZsptsCtqjreS6t69e6nct7JhsCOtVsTVRylSa174gyQU9+lYDyd7PBvgiWfreqJNnSpo5ufBZ/SIiEpRuQ12ZF0MdpRfQnoOzt5+grN34nH29hPcjksv+qJ87O0ENK3hjta1q6J17Spo7V8V/p5OnEOPiMhCGOzIKAY7KkpsahbO3UnAuTtPcO72E9yJL37QAwBvVwe08n8a9GpXQYtaVeDKefSIiEqEwY6MYrCj4opJzsL5yCc4e/sJzt15YvYKGPkpBCCwmlteq55/FTT1c0dgNTc4KDn6loioKAx2ZBSDHckVnZSJ85FPcCEyAeH3k3DjcSqKMeDWgL2dgAa+bmjm545mfu5oWsMdTf3c4eZob9lKExFVcBU62KWmpiIkJAR//fUX4uPjkZmZCVO3FgQBwcHBlii60mOwI0tLy1bj0oMkhD9IQvj9RITfT8KT9BxZ96zj5SwFvWZ+Hmjm5w4fNxWf2SMim1Uhg51Wq8WCBQvw1VdfIT3dvOd6RFHkdCfFwGBHpU0URTxIyET4g0T8eS8R4Q+ScDU6pVjz6Bnj7eqApk9DXuPqbgis5oYAbxeOxiUim1Ahg92YMWPwww8/QBRF2NnZwcvLC7GxsRAEAbVq1UJiYiLS0tLyChMEeHt7w9nZGQAQGRkp/x3YAAY7soasXA0uRyUj/H4Swh8k4nJUCu4nlOxZPX0KAajr5YIGvq5oWM0VgdXc0MDXFfV9XBn4iKhSqXDB7sCBA+jfvz8EQcDYsWPx1VdfISoqCi1atDBokbt+/TpWrVqFFStWoH79+ti5cycaN25ssTdR2THYUXmRkpWLiOgUXHn6dfVRCm4+TpXdsgfkBb7ans5o4Ov2NPC5oqGvG+r7uMLJgYGPiCqeChfsXnnlFWzbtg3PPPMMLl26BAC4cuUKmjdvbrSr9bfffsOwYcPg7++P8PBweHh4yKu9jWCwo/IsW63BzcdpuBKdjKtPA1/EoxSk51jmUQtBAPyrOqOhrysa+LoiwNsFdb1dUNfLBdXc+QwfEZVf1vj8ljVB1blz5yAIAqZNm2bW+YMGDcLYsWOxbt06LF26FO+//76c4omoHFAp7fBMTQ88U/Of/6hptSLuPknH1UdPW/ai81r2opOzin1/UQTuJ2TgfkIGDl+LNTjmZG+HOl7OCPB2QR0vFwR4O6OuV17w8+XADSKyQbJa7JydnZGdnY2QkBD07NkTAHDt2jU0bdoUgiAgIyMDKpXK4Jrff/8dQUFBaNWqFf788095tbcRbLGjyiI1Kxe349Jx83EqbsamSX8+TMy0eFnODnZS2Kvj5YKAp4GvrrczfFwZ+oio9FW4FjsdT09PadvNzU3ajo2Nhb+/v8G5vr6+AIC7d+9aomgiqkDcHO3Ryr8KWvlXMdifnq3G7bg03HycZhD4HiRmFHtNXJ2MHA0iHuV1C+fn8jT0+Xs6oVZVZ9Ss4oRaVfO2a3k6wZ1z8hFRBSUr2FWrVg33799HQkKCwT4HBwfk5ubi0qVLBYLdvXv3AABZWcXvkiGiyslFpUSLWnlLmOnLzNHkBb7YVNx4nIbIuHTcfZL3lZWrLXF56TkaXH2UN/jDGHdHZV7I04W9qgx+RFQxyAp2zZs3x/3793H16lWpK1apVKJ169a4cOEC1q1bhwEDBhhcs2rVKgBAnTp15BRNRDbAyaHg83tA3jN8j1OzcDc+Iy/oxacjMj4d957kvc5Wlzz0AUBKlrpEwa9mVSfU8HBCVWd7dvUSkVXICnY9evTAnj17EBISYjCAYvTo0Th//jx27NiBsWPHYsSIEUhPT8eGDRsQEhICQRAwZMgQ2ZUnItukUAio4ZEXojrW9zI4ptWKiEnJygt7T/LCXmR8Xvi7l5CBHJmhDyg6+DkoFajh4Yhq7o6o4eGI6h6OqC5tO6GGhyO8XVWwUzD8EZFlyRo8ERkZifr160OlUuHu3buoVq0aAECtVqNDhw74888/C/yvVRRF1KlTB3/++SeqVq0qr/Y2goMniCxDoxXxKDkT955k4N6TDEQlZeBhYubTrww8Tskus7rYKQT4uqmk0Ffd45/gpwuBvu4qqJScw4+ooqpw89gBeYMgNBoN/Pz84OTkJO1PTEzEjBkzsG3bNuTm5uYVJggICgrCqlWrUKtWLXk1tyEMdkRlI1utQXRSFh4mZiBKL/Dpwt/j1KwSD+YoKW9XB1Rzzwt/vu4q+Liq4OOm+3KEr5sK3q4qTuJMVA5VyGBXlNTUVNy8eRNqtRoNGjQwGEFL5mGwIyofstUaPErKkgJfVJJh+ItJKfvgp+OmUsLHTQVvXegzCIB5r33dVfByYRcwUVmplMGO5GOwI6oYcjVaxKVm41FyFh6nZOFRchZikjMRk5KNmORMaX+uxnr/7CoEwNPFMPDpB0BvFwd4ujrA08UBVZ0dYG+nsFpdiSq6CjuPHRERAfZ2CvhVcYJfFadCz9FqRSRk5CAmWT/46bazpP2ZuZZZkq1A+SIQn5aN+LRsRDwq+nx3RyW8XFXwdMkLe15P//R0cYCXqwM8XVTwdM4Lg14uDnC0Z5cwkTWVerBLTEyEWq2Gt7c3h/8Tkc1TKAR4u+Y9F5d/GhcdURSRkqXOC3opT8Nfcl4Yi0vNRmxqFuKebsuZz88cKVlqpGSpERmfbtb5zg52+QKg6mkAfPqlFwI9XRzgqlLys4HIgkoU7NRqNa5du4bs7GzUrl0bPj4+BsezsrLwySefYO3atXj0KO+/hE5OTnj++efx4YcfolmzZvJrTkRUSQmCAA8ne3g42aNRdbdCzxNFEWnZasSl5oU8Xdgz9jo+LRvaMugBzsjRICMn0+xl4pQKAVWc895rVWeHp9t5f1Zxss/701n3+ulxZ3u4MRASGVWsZ+xEUcT8+fOxdOlSpKamSvs7duyIxYsXo3379sjJyUHfvn1x6tQp6RqpMEGAo6Mjdu/ejd69e1vwbVRufMaOiOTSaEUkpOeYCIBZiEvNRkJ6DpIyc602CMRcdgoBVZzyQl5eAHT4509neyksVnF2QNWnoVAXCBUcPEJlpNwPnhg3bhw2bdoEwDCwAUDVqlVx/vx5rFy5EkuWLAGQt4Zsw4YNoVarcfXqVWRm5v0PztfXF9evX4eHh/FuCDLEYEdEZUmt0SIpMxcJ6Tl4kpaDhPQcJKRn40l63vaT9BwkpP2znZiRA01ZNAdagEKA1Brq7mQPd0d7uDsp4e5oDzdH5dPXefvcVP9s646z65iKo1wPnjh69Cg2btwIQRCgUqkwYMAABAQE4N69e9izZw+SkpKwePFibNmyBfb29lixYgUmTpwo/QXIzMzE/Pnz8eWXXyIuLg7r16/HzJkzS+2NERFRySjtFNJzgKhW9PlarYjULDWepGf/E/x0ITDNMBTqjltiBZCS0IpAYkYuEjNyS3S9QgDcCguDutdO9nB/+qfuuMfT466OSk43Q6XK7Ba7MWPGYPPmzahWrRqOHDmCJk2aSMeuXbuGXr16IS4uDlqtFrNnz8Znn31m9D79+/fHgQMH0KdPHxw8eNAy76KSY4sdEVUmoigiPUeDxPQcJGXkIilT92cukjNykJiRi6SMXCTr7U/KyEVSRg7UFaRl0BRXlVIKfq4qJVwdlXBRKeGmymsRdFEppdZBl6fH3XTnOeQdc1EpORVNBVCuW+zOnz8PQRDw5ptvGoQ6AGjcuDHefPNNvPvuuxAEAa+++mqh9xk7diwOHDiAK1eulLzWRERUYQmCkBdoVEr4F2POel0gTMrIeRr88gJfYkbO0239gJgXGBMz8rZzNNZpITQmLVuNtGw1opOzZN1HpVRIAVA/9OUPhC5Pv9d5x+zhorIz2HZx4HOHlYnZwS46OhpA3kAJY/T3N2jQoND7NGzYEACQkJBgbtFEREQGgbBWMZYaF0URmbmap61+eQEwJSsXKZnqvD+z1EjJzJX2pebbl5atLpeDSbLVWmSn5SA+LUf2vfLCoJ0UCp0d8gKfs0oJFwc7ODvk7XN+GgSdHez+OU//fAc7OKuUcLa3Y1i0ErODXXp6OgRBKHRJsCpVqkjbKpWq0Ps4OjoCAHJy5P8iEhERFUUQhKfBRGly8ujCaLUi0nKeBj1dGMzMRWqW2iAgphqERcOQWN4Hl+haER8j22L3dLK3g4vqn1CoC4D/hEZdGFQanFdoaFTZwcFOwcErRSj2PHaFfUP5jSYiospIoRDyBkg42gPFaCnUEUURGTmafGEvb1sXqNKyCtl++jo9W420nPLZcliYzFzN0xVULNeQo1QIcHKwg5O9XYE/nR3s4Givt62338k+75izgxJODop/tu317vH0vIo+uIVLihEREZUiQRDg8rSLs4aMWb60WhEZuRqkZ6uRmmUsEOYiPUfz9Fgu0rMNt9OeXpeerS61JetKm/rpCOzULHWpleGgVEiBTwqLDkaCo27/0z8d9UPk0/2abPNWbLEkBjsiIqIKQKH45xnDajIHWKo12rzgJ4W+XCksZuRokJGtRnqOBpk5GqTnqJGR/fTPnLxgmWFkf3nvbjZXjlqLHLUWyZklmxJHnzY7wwI1Kp5iB7uVK1fC19e3wP7Y2Fhp+6OPPir0ev3ziIiIqOwp7RTwcFbAw9neIvcTRRE5Gq3xAKgXBDNzNEjP1iAjR11oYMz7UiM9W1NhWxatyex57BQKyz2wKIoiBEGARsMfmDk4jx0REdkijTZvRLOuBVHXjZyZozH5Z0aOBlm5eQExM1eLrBwNMnLzgmVWrvbp/rzt0qTNzsCDJSPK5zx2QMFlxIiIiIhKi51e93Np0GpFZKmNB8SMXA2y8gVF3f5MKThqngbEp9v59qepy34S6WItKUZERERUWSgU/0yFUxpSUlLg8VWp3LpQZr+T7t27l2Y9iIiIiEgmLjRHREREVEkw2BERERFVEgx2RERERJUEgx0RERFRJcFgR0RERFRJMNgRERERVRIMdkRERESVBIMdERERUSXBYEdERERUSTDYEREREVUSDHZERERElQSDHREREVElobR2BahooigCAFJSUqxcEyIiIjKX7nNb9zleFhjsKoAnT54AAPz9/a1cEyIiIiquJ0+ewMPDo0zKYrCrADw9PQEA9+/fL7NfDCpcSkoK/P398eDBA7i7u1u7OjaNP4vygz+L8oM/i/IjOTkZtWvXlj7HywKDXQWgUOQ9Cunh4cG/pOWIu7s7fx7lBH8W5Qd/FuUHfxblh+5zvEzKKrOSiIiIiKhUMdgRERERVRIMdhWASqXC/PnzoVKprF0VAn8e5Ql/FuUHfxblB38W5Yc1fhaCWJZjcImIiIio1LDFjoiIiKiSYLAjIiIiqiQY7IiIiIgqCQY7IiIiokqCwa4cu3fvHv773/+icePGcHFxgaenJ9q3b48vvvgCGRkZ1q6eTQgLC8NHH32Efv36oVatWlCpVHB1dUVgYCDGjx+PU6dOWbuKNu/dd9+FIAjS17Fjx6xdJZtz//59zJ8/H+3atYOPjw8cHR3h7++Prl27Yt68ebh8+bK1q1jp5eTkYM2aNfjXv/6FGjVqSP9WNWrUCOPHj8eZM2esXcUKLzY2Fnv27MG8efPQv39/eHt7S//ujBs3rtj3279/P4YOHSp9ttSqVQtDhw7F/v375VVUpHJp9+7doru7uwjA6FdgYKB48+ZNa1ezUuvatWuh33/9rzFjxojZ2dnWrq5NCg8PF5VKpcHP4+jRo9aulk1ZunSp6OLiYvLvyMyZM61dzUrt7t27YrNmzYr8t2r69OmiVqu1dnUrLFPf27Fjx5p9H41GI06cONHk/SZNmiRqNJoS1ZMtduVQeHg4Xn75ZaSkpMDV1RULFy7EmTNncPjwYUyePBkAcOPGDQwYMACpqalWrm3lFR0dDQDw8/PDzJkzsX37dly4cAFnz57F119/jZo1awIANm7cWKL/rZE8Wq0WU6ZMgVqthq+vr7WrY5M+/vhjzJgxA+np6QgMDMQXX3yBY8eOITw8HCEhIfjiiy/QqVOnMl1Oydbk5uZiwIABuHLlCgCgRYsWWL9+Pc6ePYuDBw9i3rx5cHFxAQAsW7YMixYtsmZ1K43atWujX79+Jbp27ty5CA4OBgC0bt0aW7ZswYULF7Blyxa0bt0aALBmzRq89957JatcieIglSpdS5FSqRTPnDlT4Pjnn38upfr58+eXfQVtxIABA8StW7eKarXa6PG4uDgxMDBQ+lkcP368jGto2xYvXiwCEBs3bizOmTOHLXZlLCQkxKDVOicnp9Bz2aJden7++Wfp59CxY0ej/16FhYWJ9vb2IgCxSpUqYm5urhVqWvHNmzdP/O2338SYmBhRFEUxMjKy2C12169fl3oZ2rVrJ2ZkZBgcT09PF9u1aydlgJL0zDHYlTPnz5+XflGmTp1q9ByNRiM2adJE+ktq6h9UKl2//fabQTcHlY179+6Jrq6uIgDx2LFj4vz58xnsypBGoxEbNmwoAhBbtmzJoGBFb775pvS7v3v37kLPGzp0qHTepUuXyrCGlVdJgt1rr70mXXP27Fmj55w9e1Y65/XXXy92vdg+Xs7s3LlT2h4/frzRcxQKBcaMGQMASEpKwtGjR8uiamREz549pe3bt29bsSa2Zdq0aUhLS8PYsWPRvXt3a1fH5hw8eBA3b94EkDd4RalUWrlGtisnJ0farlevXqHn1a9f3+g1VHZEUcSuXbsAAI0bN0aHDh2MntehQwc0atQIALBr1y6IxVwgjMGunNGNsnRxcUHbtm0LPU//w+z06dOlXi8yLjs7W9q2s7OzYk1sx7Zt27Bnzx54enriyy+/tHZ1bNLPP/8MABAEAQMHDpT2JyQk4ObNm0hISLBW1WyOLgAAwJ07dwo9T/cfT0EQ0LBhw1KvFxUUGRkpPbtd1H9IdcejoqJw9+7dYpXDYFfOREREAAAaNGhg8n/BjRs3LnANlb3jx49L202aNLFiTWxDUlISZs6cCQBYtGgRvL29rVwj23Tu3DkAQN26deHm5oYff/wRzZs3h5eXFwIDA+Hl5YVGjRrhyy+/NPjPD1neyJEj4e7uDiDv74RGoylwTnh4OPbu3QsA+Pe//y2dT2Xr6tWr0rb+Z7gxcj7jGezKkaysLMTHxwMAatWqZfLcqlWrSiOdHjx4UOp1o4K0Wi0+++wz6fWIESOsWBvb8M477yAmJgadO3fGxIkTrV0dm6TVanHt2jUAgLe3N2bOnIlRo0YVmKvuxo0bmD17Nnr16oWkpCQr1NQ2eHt7Y9OmTXB2dsbp06fRvn17bNy4EefOnUNISAg+/PBDdO/eHTk5OWjTpg2++uora1fZZj18+FDaLuoz3t/fX9ou7mc8g105oj91iaura5Hn64JdWlpaqdWJCrd48WJcuHABADBs2DCTXeck38mTJ7FmzRoolUp8++23EATB2lWyScnJydBqtQCAv//+G0uXLkWNGjWwefNmJCQkICMjA8ePH5eeHzpz5gwmTJhgzSpXeoMHD8Yff/yBSZMm4eLFixg7diw6duyIvn374oMPPoCzszOWLFmCkydPolq1ataurs0qzme87vMdKP5nPINdOZKVlSVtOzg4FHm+SqUCAGRmZpZanci448eP43//+x8AwNfXF6tWrbJyjSq3nJwcTJkyBaIo4s0338Qzzzxj7SrZrPT0dGk7KysLzs7OOHr0KEaNGoWqVavCyckJ3bp1w5EjR9CyZUsAwI4dO3D+/HlrVbnSy8nJwcaNGwt90P7x48fYvHkzQkJCrFA70inOZ7zu8x0o/mc8g1054ujoKG2bM2pJ9+yKk5NTqdWJCrpy5QqGDh0KtVoNR0dH/Pzzz5wgt5R98sknuHbtGmrXro358+dbuzo2Tf/fKQCYNGmSwQP8Ok5OTli4cKH0euvWraVeN1uUnp6OPn364NNPP0VCQgLeeecdREREIDs7G8nJyTh48CC6dOmCsLAwvPDCC/j666+tXWWbVZzPeP1nU4v7Gc9gV464ublJ2+Y0ver+52xOty1ZRmRkJPr164fExETY2dnhp59+Qrdu3axdrUrt2rVr+PTTTwHkzZyv30VBZU//3ykAJmff7927tzQILDQ0tFTrZas++OADnDx5EgAQHByMRYsWoXHjxnBwcIC7uzv69u2Lo0ePomfPnhBFEbNnz8Zff/1l5VrbpuJ8xuu3jBf3M56TD5Ujjo6O8PLywpMnTwwesjQmMTFR+sHrP2RJpSc6Ohp9+vRBdHQ0BEHA2rVrMWTIEGtXq9JbvHgxcnJyUK9ePWRkZOCnn34qcI7+g/tHjhxBTEwMAGDQoEEMghamUqng4+ODuLg4AKb//XF0dIS3tzdiYmKk88lyRFHE2rVrAQCBgYEYO3as0fOUSiUWLFiALl26QKvVYv369Vi8eHFZVpVgOGCiqM94/QETxf2MZ7ArZ5o2bYqTJ0/i1q1bUKvVhU55ohuVBnCajbIQHx+Pvn37SvNELVu2TJokmkqXrkvizp07GDlyZJHnL1iwQNqOjIxksCsFzZo1w7FjxwDA6PQa+nTHOYmx5T1+/FiaM1C3xmhh9Ad36X9+UNlp2rSptF3Uz0DOZzy7YsuZLl26AMhrhv3jjz8KPU9//rTOnTuXer1sWXJyMv71r39JcxB99tlnmDZtmpVrRWQ9+o8fmJoUNyUlRZrCqWbNmqVeL1ujH5bVarXJc3Nzc41eR2UnICAAfn5+AAw/w405ceIEgLy/N3Xr1i1WOQx25cwLL7wgba9bt87oOVqtFhs3bgQAVKlSxWBZK7KsjIwMDBgwAH/++ScAYO7cuXj33XetXCvbsn79eoh561oX+qU/oOLo0aPS/uL+g0jmGT58uLS9Y8eOQs/bsWOHNEqza9eupV4vW+Pp6SlNNnz27FmT4U4/SAQEBJR63aggQRCkx3euXbsmTfSd37lz56QWuyFDhhR7aicGu3Lm2Weflf4BDA4OxtmzZwuc89VXX0kzUc+cORP29vZlWkdbkZOTg6FDh0pLts2cORMff/yxlWtFZH0tWrRA//79AQBbtmzB4cOHC5wTExOD9957D0De1A6FrX1NJadQKDBgwAAAec8A649C1peYmGjwH1L9ZeCobM2aNUtafnL69OkFpjLJzMzE9OnTAeS1rM6aNavYZbA9thz65ptv0LlzZ2RmZqJfv374v//7P/Ts2ROZmZn46aefsHr1agB5D8v+97//tXJtK6+RI0fi4MGDAIBevXph4sSJBWbX1+fg4IDAwMCyqh6RVS1ZsgRnz55FUlISBg4ciFmzZiEoKAhOTk64cOECPv30U+kB8QULFrArtpTMmzcPu3btQkZGBj744AP88ccfGDt2LOrVq4esrCycO3cOS5Yswf379wHkjVQ2NZKZCnfq1CncunVLeq17zAAAbt26hfXr1xucP27cuAL3CAwMxOzZs/HZZ58hLCwMnTt3xrvvvov69evj9u3bWLRoEcLDwwEAs2fPLtm6viKVS7t37xbd3d1FAEa/AgMDxZs3b1q7mpVaYd/7wr7q1Klj7SrbrPnz50s/h6NHj1q7Ojbj5MmTYrVq1Qr9OyEIgvjee+9Zu5qV3qFDh0Rvb+8i/43q1auXmJCQYO3qVlhjx44t1mdCYTQajThhwgST106cOFHUaDQlqie7YsupQYMG4dKlS3jzzTcRGBgIZ2dnVKlSBe3atZMSfYMGDaxdTSKyYV26dMGVK1cwf/58tGzZEu7u7nB0dERAQADGjx+PP/74w2CUMpWOPn364Nq1a1i0aBF69OgBHx8f2Nvbw8nJCQEBARgxYgR27tyJkJAQVK1a1drVtXkKhQLBwcHYu3cvhgwZAj8/Pzg4OMDPzw9DhgzBvn37sGbNGigUJYtogigaWX+EiIiIiCocttgRERERVRIMdkRERESVBIMdERERUSXBYEdERERUSTDYEREREVUSDHZERERElQSDHREREVElwWBHREREVEkw2BERERFVEgx2RERERJUEgx0RERFRJcFgR2RFgiBAEAR88MEH1q4KUYWj+/uj/9WjRw9rV8sqjh07ZvT7wX9bbA+DHZEZCvtHU6lUwtPTEwEBAejWrRvefPNN/PLLL8jJybF2lYmoCD179pT+Lvfr189i9+3Ro4d0X3PZejAly2GwI5JBo9EgMTERd+/excmTJ7FkyRK8+OKLqFWrFj7++GOo1Wqr1Ev3wcIPCcu6e/eu9AG8fv16a1dHtsrSqvPaa6/h77//xt9//41169aZdc29e/dw/Phx6fXhw4cRHR1dWlUsde3bt5e+B3///be1q0NWpLR2BYgqmtdeew2vv/669DotLQ2JiYm4dOkSDh8+jJCQEMTFxeH999/Hb7/9hj179sDHx8fovURRLKtqE1Vavr6+eOaZZ4p1zaZNmyCKIlQqFTQaDdRqNTZv3ox33nmnlGpZulxcXIr9PaDKiS12RMWk+xDRfXXo0AH9+/fHu+++i4MHD+Ly5cto3bo1AODChQsYOnQou2aJyplNmzYBAAYOHCh1w+r2EVVkDHZEFta0aVOcPn1aCnenT5/GihUrrFwrItI5d+4cbty4AQAYNWoURo8eDQC4fPky/vzzT2tWjUg2BjuiUuDk5IRNmzZJD09/+eWXyM3NLXBeUc84JSUlYeHChejYsSOqVq0Ke3t7+Pj4oGnTphg6dChWrVqFx48fS+ePGzcOgiBIzw4dP368wICPunXrGpSRnp6OrVu3YtKkSWjVqhU8PDykcrp3744vv/wSaWlpJt9v/vcRGhqKkSNHolatWlCpVKhZsyZeffVVREREmPX9u3z5MqZPn47mzZtL77t69ero06cPPv/8czx69KjQa2NiYjB37ly0a9cOnp6eUKlU8Pf3x4gRIxASEmJW+YW9x4CAAOn1+PHjzRqBqNVqsXnzZgQFBaF69epwcHCAj48PevbsiZUrV1qkNffGjRuYPn06nnnmGbi5ucHBwQF+fn5o1aoVJkyYgK1btyI7O1s6v27dugYP9n/44YcF3su4ceMMynj06BFWrlyJF198EQ0bNoSLi4v0sx0yZAi2bt0KrVZbaB31ByAdO3YMWq0Wa9euRc+ePVGtWjUoFIoCZZaWjRs3AgCqVq2KAQMG4IUXXoCbm5vBMaIKSySiIh09elQEIAIQ58+fb/Z1/fr1k647ffp0geOm7nn16lXRz89POqewr2XLlknXjB07tsjz69SpY1BO9+7di7wmICBAjIiIKPR96r+PFStWiEql0uh9nJ2dxePHjxd6H7VaLb755puiIAgm6zN27Fij12/evFl0cXExee3EiRPF3NzcQutQ1Hs09ZX/5/jkyROxc+fOJq9p0qSJePfu3WLXR2fbtm2ig4NDkXX7+++/pWvq1KlT5Pn632O1Wi0qFIoir+nbt6+YmppqtJ76f4f2798v9unTx+yfa2FK8ncyOztb9PT0FAGIU6ZMkfbr/u74+vqW6PdDn/7fKXPpzu/evbussvPfrzjfG6ocOHiCqBT16dMHBw8eBACcPHkSnTp1MvvaV199FdHR0bC3t8fkyZPRv39/VK9eHVqtFg8fPsS5c+ewY8cOg2sWLlyIt99+G+PHj0dYWBjatWtXYJSgg4ODwWu1Wo3mzZtj8ODBaNeuHfz8/CCKIu7du4cdO3Zg27ZtiIyMxAsvvICLFy/C0dGx0DofOHAAFy5cQPPmzTFz5kw0b94cmZmZ2LFjB7755htkZGTg1Vdfxc2bNwvUAwCmTJmCtWvXAgBq1KiBN954A506dYKHhwfi4uJw4cIFbN++3WjZ27Ztw6uvvgpRFFGvXj288cYbaNq0KXx8fHD37l0EBwdj3759CA4Ohru7O77++muzfg46f//9N6Kjo/Gvf/0LAPDxxx9jyJAhBuf4+vpK2xqNBgMHDsTZs2cBAN27d8cbb7yBgIAAREdHY+3atdi5cyciIiLQu3dvXLx4Ea6ursWq0+PHjzF+/Hjk5OTA19cXb7zxBjp06ABvb29kZmbi1q1bOH78OHbu3Glw3cGDB5GTk4PmzZsDKDggCMhrzdIRnw7y6dWrF/r374/mzZvDx8cHqampuHPnDr7//nucPXsWhw4dwrRp07BhwwaT9X733Xdx6dIlDB48GOPGjUOdOnXw+PFjpKSkFOv9l8SePXuQkJAAAFIXrG57w4YNiI2Nxe+//46BAweWel2ISoWVgyVRhVDSFruQkBDpugkTJhQ4Xtg9b9++bbRFLj+tVismJCQU2K9rMTDnf/83btwwefzQoUNSa82aNWuMngO9VpegoCAxOzu7wDkff/yxdM6vv/5a4PiuXbuk4x07dhQTExMLrdP9+/cNXsfFxYkeHh7S97mwFpf/+7//EwGICoVCvHbtmol3bVxkZKRUx3Xr1pk8d/ny5dK5Y8aMEbVabaH1ASC+8847xa5PcHCw0Ra5/DIyMsSMjIwC+839ndZqteLNmzdNnjNv3jwRgCgIgtHfKf2/QwDE9957z+T9zFGSv5NDhgwRAYh169Y1+JloNBqphfyll16SVS+22JE18Rk7olLk5eUlbScmJpp9XUxMjLTdrVu3Qs8TBMGgZaUkGjZsaPJ4nz59MHjwYAAo0PKTn6OjI9atW2e0NW7GjBnS/pMnTxY4/tlnnwEAnJ2dsX37dlSpUqXQcvz9/Q1er1q1CsnJyahZsyZWrlwJpdJ4Z8SHH36ImjVrQqvVlvqzVLoBMz4+Pli+fLnRyWo//PBDNG7cGADw/fffGzwHZw7d70nVqlVNTnXh5OQEJyenYt1bnyAIaNCggclz5s2bB29vb4iiiN27d5s8NzAw0Cpz5z158gT79u0DAPz73/82+JkoFAr8+9//BgD89ttvSEpKKvP6EVkCgx1RKdLvWktNTTX7uho1akjbZT0RblxcHG7evInLly9LX7p5+P766y+T1/bt29egO1Kfm5ubFCLv3LljcOzJkyc4d+4cAODll1+Gn59fseqsCxIDBw6ESqUq9DylUomOHTsCgNRFWhqio6OlgSIjRoyQHsw3Vp/x48cDyAv+xR2Rqfs9SUxMxK5du2TUuHi0Wi2io6Nx/fp16XckIiICtWrVAlD078nLL78MOzu7sqiqgS1btkiDmPS7YXV0+7KysvDzzz+Xad2ILIXP2BGVIv0w5+7ubvZ1AQEB6Nq1K06ePInFixfjwIEDGD58OHr06IEOHTrA2dnZovU8ffo0li5dipCQEOn5I2Pi4+NN3kfX+lQYT09PAAVD7sWLF6XnuLp27WpOlSUajQYXL14EAHz33Xf47rvvzLpOv1XU0i5fvixtP/fccybP1T9++fJlKXiaY/DgwahSpQqSkpIwdOhQ9OjRA4MGDUK3bt3QqlUri4YnURTxww8/IDg4GOfPn0dmZmah5xb1e9KiRQuL1as4dM/+tWnTBk2aNClwvGXLlnjmmWdw+fJlbNy4EZMnTy7rKhLJxhY7olKk/wGnCzXm2rJli/Qhf/XqVSxYsAC9e/dGlSpV0K1bN3z77bfIysqSXccPPvgAXbp0wbZt20yGOgAmP8wBFBk4FYq8f3I0Go3Bfv3vk35rpTkSEhJKtHRbRkZGsa8xl/73sbAWTJ3q1asbvc4cXl5e2L17N2rWrAlRFHH06FG89dZb0lQvw4YNw549e4pXeSOysrIwYMAAvPrqqzh27FiRvwdFHZf7+EBJREREICwsDIDx1jqdV199FUDef3YiIyNLVJZ+F6/uPyym6J9TnPVliYxhsCMqReHh4dJ2o0aNinVtzZo1cebMGYSEhOD1119Hs2bNIAgCcnNzcfLkSbz22mt45plnpIlWS+Lw4cP48MMPAQD16tXDypUrcenSJSQlJSE3NxeiKEIURbz//vslLqO06YfESZMmGayXaepLN1q5tJX2B3XXrl1x69YtbN68Gf/+97+l7tCUlBTs2LEDgwYNwvPPPy8ryC5cuBD79+8HkDe6d9u2bbh16xbS0tKg0Wik3xNda2tRYcYa3bD6z1S+9dZbBebt0329++67APLeQ0mfw9R/nrGokAvkzSWp4+LiUqIyiXTYFUtUig4dOiRtd+nSpUT36N27N3r37g0g71m0kJAQrF69GkeOHMHt27fx8ssvGwTI4vj+++8B5LWgnDt3rtA1bYvbklRc3t7e0rapyYeN0W8JFUWxXKyXqV8n/QmkjdHvEi5uq66Oo6MjRo0ahVGjRgEAIiMjsXfvXixbtgw3btzAgQMHMHfuXCxevLjY9xZFEWvWrAGQFyKPHDkitbzmV9q/JyWl1Wrxww8/FPu6TZs2Yf78+cW+Tv/nGBMTg3r16pk83xK/A0Q6bLEjKiWXL1/G4cOHAeSN4mzXrp3se3p5eeHll1/G4cOHpZGqFy9exM2bNw3OM7eV6MqVKwCAnj17FhrqAEhdWKWldevWUp1PnDhRrGsdHBzQrFkzAHndZ6XJ3O+rfrg8f/68yXMvXLhg9Do5AgIC8MYbbyA0NFRqwdu2bVuJ7pWQkCAFj5deeqnQUJeWlobr16+XrMKl7OjRo3jw4AEAYPr06diyZYvJr1mzZgEAbt++XaLfKf1nCP/4448iz9cfNGOt5w+p8mCwIyoFmZmZGDNmjNQl9fbbbxc6BUdJ6VrxgIIPq+smES5q+gzds2n6XUH5hYeHFxlO5PL09JQmb962bRuio6OLdb0u5F67dg0HDhyweP109CdnNvW99fPzkx7O37ZtW6FLsmk0GmnUc9WqVdGmTRvLVRZ5A3bat28PwPiABnN+T/SfXzT1e7JmzZoSPetYFnRdqnZ2dnjvvffwyiuvmPyaO3eu9Pe1JN2x+n83f/zxxyLP37x5s9FriUqCwY7Iwq5evYouXbpI3aPdu3fHa6+9Vqx7XLx4URrpaYwoitK6p8bWf9UNQLhz547J551004+cOnUKt27dKnA8Li5Oepi8tOmebcrIyMBLL72E5OTkQs99+PChweuZM2dKU8uMHz9eaokszN69e3Hp0qVi19HLy0uai+/27dsmz502bRqAvO/hjBkzjJ7z4Ycf4urVqwCAyZMnm5yqxZgDBw6Y7LpOTk6WWgT117nV0f2emHovPj4+0pyCW7ZsMRoCQ0NDy+1zmOnp6fj1118B5HUlFzWYBch7NKB79+4A8oJ5/vdc2LrLOm3btpUGPu3cuRNbtmwptKxNmzbht99+A5D3uEbr1q2Nnqdb35eDK6gofMaOqJhiY2MNprNIT09HYmIiLl26hMOHD+PQoUNSmOrQoQO2b98Oe3v7YpVx8eJFjB8/Hu3bt8egQYPQpk0bVK9eHbm5uYiMjMS6deuk5/cGDx5cYCRpp06dsG7dOsTGxuKtt97C6NGj4eHhAQCwt7dHnTp1AABjxozBb7/9hvT0dHTv3h3/+9//0LZtWwDAmTNn8PXXXyMmJgYdO3Ys1XnfAGDQoEGYOHEigoODcebMGTRt2hRvvPEGOnfuDHd3d8THxyMsLAxbt25Fy5YtDeb3q1atGjZs2IAXX3wRjx49Qrt27TBu3Dj0798ftWrVQm5uLh4+fCgtSXbnzh389ttvxe72UiqVaN++PU6fPo21a9eidevWaNWqlfTz9fT0lJ6R+s9//oMffvgBZ8+exbp163Dv3j28/vrrCAgIwKNHj7B27VopcNSvX79EwWjLli0YNGgQ+vbti379+uGZZ56Bp6cnUlNTcfnyZSxfvhxRUVFSffLr1KkTIiMjsXv3bnz33Xfo3Lmz1Irn7u4OX19fKBQKjBo1CitWrMClS5fQpUsXvPXWW2jYsCGSk5Oxb98+rFy5Eq6urvDz85M1mKc0/Prrr1KL6fDhw82+bvjw4Th8+DCSkpKwe/duvPTSS8Uqd/Xq1Xj22WeRmZmJUaNGYceOHXjxxRcREBAAQRBw584dbN++XVoiz8XFBatXry5WGURGlfVSF0QVUf7lkIr68vHxERcuXFjkYuIoZNmfdevWmVVOp06dxPj4+AL3TU1NFevVq2f0mjp16hicO378+ELvb2dnJy5ZskScP3++ySWSCnsf+RW11JlarRbfeOMNURAEk++7sMXid+/eLS3wbupLoVCIR44cMVnXwuzZs6fQ+uV//0+ePBE7d+5ssi5NmjQR7969W6K66BauL+rrP//5j6jRaApcHx4eLqpUqiK/x0lJSWKrVq0Kvb+np6d4/Phxkz9f/b9DR48eLdH7zc+c37s+ffpIS51FRUWZfe+YmBhpKb2BAwcaLTf/36X8zpw5I9asWbPIn0/NmjXFc+fOmbxXnTp1irVMmbl/J6nyYYsdkQwKhQJubm7w8PBAnTp10LZtW3Tt2hUDBw40uqyWuUaOHIlq1arh0KFDCA0NRVRUFB4/fgy1Wg1fX1+0adMGL7/8Ml555RWjD7O7urrizJkz+PTTT3Hw4EHcu3ev0Oku1q5di169emH16tW4ePEicnJyUL16dXTr1g1vvPEGnn322TJb/snOzg7Lli3D+PHj8d133+HYsWOIiopCTk4OvLy80KJFCzz//POFdg8PGjQIkZGR+P7777Fv3z5cuXIFCQkJUCqVqF69Opo1a4ZevXrhxRdfLLAsmbkGDBiAw4cP45tvvkFoaCji4uKk1Qzy8/T0xIkTJ/DDDz/gxx9/RHh4OBISEuDu7o7mzZvjxRdfxOTJk0v8u7J48WL07dsXR44cwaVLl/Do0SPExcXBzs4O/v7+6NixIyZNmlToiOxWrVrh7Nmz+OKLL3D69Gk8fvzYaFerh4cHTp8+ja+//hrbtm3DzZs3oVQq4e/vjwEDBmDmzJnSII3yJCoqCkeOHAEAdOzYsVgrmlSrVg2dO3fGyZMn8fvvvyMuLs7kACNjOnbsiJs3b2LDhg3Yu3cvwsPD8eTJEwB53fqtWrXCwIEDMW7cOIPnN4nkEETRjNkTiYiIyhnd82bz58+3ytqz5Rm/N7aLLXZERFSh6T/36uLiYnSgSGWXnp5e4pUyqHJhsCMiogpt1apVWLVqFYC8UejHjh2zboWsIDQ0FD179rR2Nagc4HQnRERERJUEn7EjIiIiqiTYYkdERERUSTDYEREREVUSDHZERERElQSDHREREVElwWBHREREVEkw2BERERFVEgx2RERERJUEgx0RERFRJcFgR0RERFRJMNgRERERVRL/D5xRD0kvtMOoAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "39aa16a7-64dc-46dd-ad1b-6a1fb9126e13",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.621991Z",
     "start_time": "2024-11-20T06:58:11.619653Z"
    }
   },
   "source": [
    "# 计算273-373K\n",
    "D_max = calculate_distance(1, 273)\n",
    "D_min = calculate_distance(1, 373)\n",
    "print(D_min, D_max)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5568036748267745 1.0394281216033263\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "aaa5b082-03fb-48d5-8491-329246e69b8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-20T06:58:11.633381Z",
     "start_time": "2024-11-20T06:58:11.631976Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
